1. Field of the Invention
The present invention relates to a neural network and generation method, and further relates to an image area attribute discriminating method and device for use with a neural network. This image area attribute discrimination may be used in image processing in, for example, copying machines.
2. Description of the Related Art
Neural networks accomplish information processing by learning many examples. Since neural networks are capable of high speed processing of complex information, they have been used in various fields such as image recognition, voice recognition, voice synthesis, automatic translation, and the like. An example of such uses is image area attribute discrimination.
In digital copying machines, for example, various image processes are used to improve image quality relative to multi-level digital images obtained by reading a document image. Such image processing is used for various types of documents. For example, edge enhancing processing and binarization processing are performed to sharpen text characters of text images, processing stressing halftone gradient qualities are performed on photographic images, and smoothness processing is performed on dot images to prevent moire.
There are occasions when text images, photographic images, dot images, and the like are mixed in a document. In such instances, the document image must be divided into the respective image areas. When making area divisions, a document image is divided into hypothetical minute areas (block areas), and the image data contained in each block area is sequentially extracted, and the attributes of each block area are discriminated based on the extracted image data.
Japanese Unexamined Patent Application No. HEI 4-114560 discloses a method wherein image data corresponding to block areas of 64.times.64 pixels are extracted from a document image, and histogram feature quantity and line density feature quantity are extracted based on the extracted image data, and said data are input to a neural network to discriminate attributes.
Inoue et al. propose a method in "Image Area Separation Methods Using Neural Networks" (Japan Simulation Society, 13th Simulation Technology Conference, June, 1994), wherein average luminance and maximum density difference are extracted as feature quantities from small areas of 8.times.8 pixels, and said data are input to a neural network to discriminate attributes.
The former method, however, cannot accurately discriminate whether or not an area is a halftone dot area via a neural network because when a histogram feature quantity and a line density feature quantity are input to a neural network, the information expressing periodicity of pixels is not contained in said feature quantities. Thus, a document which contains halftone dot areas pose a problem insofar as block area attributes cannot be accurately discriminated.
Furthermore, the latter method is unable to accurately discriminate whether or not an area is a halftone dot area or a photographic area even when the average luminance and maximum density difference are input to a neural network as feature quantities.
Conventionally, a single image datum, which corresponds to a single target area that is to be discriminated for attributes, is extracted, and attribute discrimination is accomplished based on said single image datum, such that discrimination errors occur in the relatedness of the actual type of image of the target area and the target area size (magnitude) and, thus, accurate discrimination cannot be accomplished.
When, for example, the target area of an image is a halftone dot area and the size of the target area is relatively small compared to the halftone period, there is the possibility that a dot image will be erroneously discriminated as a photographic image (variable density image) because information related to halftone dot periodicity is not contained in the image data. Furthermore, when the image of the target area is a text image and the text is large, and even when halftone dot lines and dots are thick in the case of dot images, there is a possibility of erroneous discrimination as a photographic image because the halftone dot features and text of the target area are not input.
Three perception levels comprising an input level, intermediate level, and output level, or improvements thereof are used as neural networks in the previously described methods. Furthermore, various feature quantities extracted from the image data of block areas are used as input signals to the neural network. That is, attribute discrimination by neural networks is conventionally accomplished by combining various feature quantities having a physical meaning.
When, however, area attributes must be discriminated in real time as in the case of copying machines, the circuitry for extracting feature quantities becomes complex and the construction of the circuits themselves becomes difficult, which presents disadvantages in terms of processing speed, flexibility, and cost. It is extremely difficult to determine threshold values for each feature quantity of a variety of documents to be copied; extensive experimentation and knowhow are required, such that a good deal of time and effort are necessary for the experiments for threshold value determination.
Speaking in general, when neural networks are used in new fields, it is often unclear what data should be input as feature quantities to accomplish discrimination by the neural network. In such instances, it is anticipated that accurate discrimination results cannot be obtained by a neural network without extensive circuitry for extracting feature quantities and a great deal of time expended to create programs.